Classification of Ultrasound Liver Image Using NLM - Vaishnavee V. Rathod, Kamalkumar Kashyap (International Journal of Advanced Science and Technology, 2020)
The conception behind medical image classification is to categorize the ultrasound images formedical diagnosis purpose. Tissue classification with ultrasound has become very importantnowadays for the analysis of ultrasound images. In this paper, a liver ultrasound image classificationmethod has been proposed for diagnosis of liver disease. The diagnosis system includes three steps:Preprocessing, feature extraction and classification. Preprocessing of liver ultrasound images isperformed by Non-Local means filter. Fuzzy c-mean clustering method with threshold Segmentation isapplied on preprocessed image for segmentation purpose. For appropriate classification, properselection of features of the image is required. By applying principal component analysis features areextracted and selected. Then ANFIS classification technique is implemented for classifying the liverultrasound image as Heterogeneous, Normal Fatty & Abnormal Fatty liver image. A preciseclassification rate of 96% is achieved using ANFIS.